Assessing artificial marketing intelligence

Foundational marketing knowledge, from theories of consumer behavior to segmentation frameworks and pricing models, supports effective marketing decisions, yet managers often struggle to access and apply it in practice. This paper evaluates whether large language models (LLMs) can function as scalable knowledge systems that make foundational marketing knowledge broadly accessible. Specifically, we assess what LLMs “know” about marketing and how effectively they can reason with and apply that knowledge. We construct a comprehensive dataset of over 30,000 questions drawn from instructor materials of 25 widely used marketing textbooks and assess LLM performance across three dimensions: domain knowledge, reasoning capabilities, and user interaction. Across models and providers, current LLMs achieve high accuracy (84%–94%), demonstrate near-complete coverage of marketing topics, and show strong performance on recall and understanding. Performance declines on higher-order reasoning tasks, though newer reasoning models close these gaps substantially. Experimental manipulations of these questions suggest that AI performance reflects more than simple memorization. In a human benchmarking study, LLMs substantially outperform all sampled human respondent groups, while showing complementary strengths relative to more advanced human reasoning. These findings indicate that LLMs have crossed an important capability threshold: they now provide reliable, on-demand access to much of the discipline’s foundational knowledge, with important implications for how marketing knowledge is disseminated, taught, and applied.